stone soup data collection w/ cycletracks

SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY

Building the Technology Potfor the Stone Soup Method

of Data Collection: Facilitating Cooperation in the Face of

Scarcity

Elizabeth A. Sall

Transportation Research Board Annual Meeting in Washington, D.C.

Tuesday January 15th, 2013

DATA.But travel models need…


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I AM A TRAVEL MODELER.

Caveat: I am not a data collection or surveying expert

So what am I going to talk about?


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Story : We needed data for something we had never seen collected before. And we didn’t have much money or time.

…so we built this app called CycleTracks

Route Choice Data Collection Choices Considered


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Cost per

Record

Cost per Respond

ent

Respondent LOE

Data Precisio

n

Data Qualit

y

RP or SP

Web-based stated preference

$ $ High SP

CATI Route recall

$$$ $$$ High Low Low RP

Personal GPS

$ $$ Med High Med RP

Bicycle GPS

$ $$ Med High High RP

Smart Phone

$ $ Low Med Med RP

Publicity! Advertising! Stickers!

CycleTracks: from coder to cyclist


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iTunes Store

Android

Market

Amazon EC/2 Server running Apache

CycleTracks Data: from cyclist to analyst


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mySQL

JSON

PHP PHP

Bay Area Participants (if they noted their home ZIP)

CycleTracksN-366

BATSN=153

Age Mean 34 33

Gender Female 20% 36%

Cycling Frequency Daily Several Times/Week Several Times/Month Less than once a month

48%36%13%3% N/A


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Data Quality: some good, some bad


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Urban Canyon Effect

Downtown

Haight Ashbury

vs


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GPS Signal at Beginning of Trip


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Not on a Bike


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Post Processing Warranted

Gaussian smoothing

Activity & mode detection

Map matching

5,178 traces497 users

3,034 bike stages

366 usersh

(Schüssler & Axhausen 2009)

~60% of submitted datauseful


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Unintended Benefit: Scalability

It works anywhere you can get a satellite signal

Database and cloud server highly scalable

Web interface for data minimizes human resources

Data cleaning open-source

Cost for data: Keeping server on Promotion


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Where do people use CycleTracks?


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*based on optional homeZIP field, NOT TRIP LOCATION

Agencies using Cycletracks:1. San Francisco2. Monterey Bay, CA3. Austin, TX4. Seattle, WA5. Fort Collins, CO6. Twin Cities, MN7. Raleigh, NC8. Salt Lake City, UT

Where?Where it’s advertised the most.

Place Users* Trips*

San Francisco 665** 11,458

Austin 276 2,950

Fort Collins 126 1,560

Seattle 108 1,175

Minneapolis 67 1,326

Oakland 26 127

Saint Paul 23 449

San Jose 22 70

Santa Cruz 17 254

Berkeley 14 127


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*based on optional homeZIP field, NOT TRIP LOCATION** compared to 153 cyclists in the 2000 HH Travel Survey

When did new users submit first trip?

• New user registrations directly correlates with publicity


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020406080

100120140160180200

Cycletracks New User's First Trip Submissions

San Francisco Monterey

AustinSeattle

Twin Cities

Fort Collins

Many just try it out , but half use it for a while


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One Day Day - Week Week - Month 1 - 3 Months 3 - 6 Months 6 - 9 Months 9 - 12 Months Over a Year0

100

200

300

400

500

600

700

800

900

1000

41%

11%

16%15%

8%

3% 3% 3%

Users by Duration of Use

Broad Spectrum of Users

• Half of users submit > 5 trips• 10% of users submitted > 20 trips• 40 users submitted >100 trips (Max =

685)


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1 2-5 6-10 11-15 16-20 21-7000

100

200

300

400

500

600

700

80031%

20%

8%

5%3%

10%

Users by Trips Submitted

Capturing Infrequent Cyclists

• 20% (500+) users infrequent cyclists (10% of trips)


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Less than once a month Several times per month Several times per week Daily

187 411 852 853577

2,036

9,122

13,506

Trips and Users by Cycling Frequency

Users Trips

All Open Source

• GPL3 License• Code on GitHub• Fork us!

www.github.com/sfcta


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Bonus Benefit: Transferability+ 750 users +8,500 trips


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AggieTracks~35 users

Cville Bike mApper~120 users/1500 trips

Cycle Atlanta~ 400 users/4500 trips NuStats PaceLogger

Rolling their own from our code:

Cycle Lane~ 200 users/2500 trips

Combined Reach: ~44,000 trips


22http://goo.gl/maps/DuqGh

Issues - Bias

Tradeoff between bias and quantity But bias can be dealt with if quantity is

high enough.Which biases are acceptable and when?

i.e. does income affect how adverse to biking up hills you are (vs. biking around them) ?

What biases can we undo with technology?


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Issues - Bias


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NHTS Sample Rate Smartphone Ownership Effective Probable Response Rate0%

20%

40%

60%

80%

100%

120%

140%

Race/Ethnicity

White Black Hispanic

Sources: 2009 NHTS (NHTS Sample/1,000 population) ; Pew 2011 Smartphone Survey; Census

People who answersurveys

People who have Smartphones

People who answerSurveys over theirSmartphones

x =

Does…

…if so, this looks pretty good.

?

Issues - Bias


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…and this looks even better.


50%

100%

150%

200%

250%

Age Group

18-24 24-34 35-44 45-54 55-64 65+

Issues - Bias


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…but looks like income exacerbates the divide.


50%

100%

150%

200%

250%

300%

Total Household Income

<$10k $10k-<$20k $20k-<$30k $30k-<$40k $40k-<$50k $50k-<$75k $75k-<$100k $100k+

Issues - Recruitment

Recruitment can be difficult Small publicity campaigns --> Small

datasets Areas most successful in recruiting users

had large publicity campaigns App needs to have value itself:

Monetary value Feel like ‘they are helping’ something

they care about Fun (at least not painful) to use


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Cliffs Notes

All we did was build a little phone app: Very tiny investment (<$20,000 total ) for

CycleTracks Yielded 35,000+ records Open source policy has afforded 8,500

more and counting


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Lessons Learned

Think about ways to get data to come to you.

Reach far with small levels of investment.Be open. Open-source works!Set aside real money to: Maintain and grow the app and associated

scripts Advertise what we have done with it/develop

a communityDevelop App under your Apple Developer ID

Changing is painfulUse an API interface rather than have the

app hard-coded to a database More flexible in case others want to

contribute data


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Thanks!

Credits: Lisa Zorn, Billy Charlton, Matt Paul

elizabeth [at] sfcta [dot] orgwww.sfcta.org/modeling

www.sfcta.org/cycletrackshttp://github.com/sfcta

Another story: We had a lot of unorganized data collected by a zillion projects, agencies, consultants…and wanted to make sense of it.

…so we built this app called CountDracula


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CountDracula

https://github.com/sfcta/CountDracula

https://github.com/sfcta/CountDracula

stone soup data collection w/ cycletracks

Technology

cycletracks data

cycletracks new users

place users

respondent data data

data quality

users infrequent cyclists

trips max

frequencyusers trips